Analyzing apparatus and analyzing method

ABSTRACT

An analyzing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to calculate a tissue characteristic parameter value with respect to each of a plurality of positions within a region of interest, by analyzing a result of a scan performed on a patient. The processing circuitry is configured to determine a measurement region in the region of interest by performing an analysis while using the tissue characteristic parameter values. The processing circuitry is configured to calculate a statistic value of the tissue characteristic parameter values in the measurement region.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2016-144697, filed on Jul. 22, 2016, andJapanese Patent Application No. 2017-140280, filed on Jul. 19, 2017; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to an analyzing apparatusand an analyzing method.

BACKGROUND

In recent years, various types of medical image diagnosis apparatusesare configured not only to express, in an image, a tomographic view of atissue in a patient's body, but also to express, in an image, aparameter indicating a characteristic of a tissue (which hereinafter maybe referred to as “tissue characteristic parameter”). For example, anultrasound diagnosis apparatus uses technology called elastography bywhich a distribution of firmness levels of a tissue is expressed in animage.

Further, when a tissue characteristic parameter is expressed in animage, quantitative information is provided by measuring parametervalues in a desired region included in the image. For example, by usingelastography implemented by an ultrasound diagnosis apparatus, fibrosisof the liver is expressed in an image, so as to categorize each offibrosis regions to be in one of fibrosis stages according to the degreeof firmness of the region.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary configuration of anultrasound diagnosis apparatus according to a first embodiment;

FIG. 2 is a drawing for explaining a process performed by an index valuecalculating function according to the first embodiment;

FIG. 3 is a chart for explaining a process performed by a determiningfunction according to the first embodiment;

FIG. 4 is a drawing for explaining a process performed by thedetermining function according to the first embodiment;

FIG. 5 is another drawing for explaining the process performed by thedetermining function according to the first embodiment;

FIG. 6 is a flowchart illustrating a processing procedure performed bythe ultrasound diagnosis apparatus according to the first embodiment;

FIG. 7 is a drawing for explaining advantageous effects achieved by theultrasound diagnosis apparatus according to the first embodiment;

FIGS. 8A and 8B are drawings for explaining a process performed by adetermining function according to a second embodiment;

FIG. 9 is a flowchart illustrating a processing procedure performed byan ultrasound diagnosis apparatus according to the second embodiment;

FIG. 10 is a flowchart illustrating a processing procedure performed byan ultrasound diagnosis apparatus according to another embodiment;

FIG. 11 is a drawing for explaining a process performed by an indexvalue calculating function according to said another embodiment;

FIG. 12 is a flowchart illustrating a processing procedure performed bythe ultrasound diagnosis apparatus according to yet another embodiment;

FIG. 13 is a chart for explaining a process performed by a determiningfunction according to said yet another embodiment;

FIG. 14 is a drawing for explaining a process performed by an ultrasounddiagnosis apparatus according to yet another embodiment; and

FIG. 15 is a block diagram illustrating an exemplary configuration of aninformation processing apparatus according to yet another embodiment.

DETAILED DESCRIPTION

It is an object of the present disclosure to provide an analyzingapparatus and an analyzing method that are able to analyze a tissuecharacteristic with an excellent level of precision.

An analyzing apparatus according to an embodiment includes processingcircuitry. The processing circuitry is configured to calculate a tissuecharacteristic parameter value with respect to each of a plurality ofpositions within a region of interest, by analyzing a result of a scanperformed on a patient. The processing circuitry is configured todetermine a measurement region in the region of interest by performingan analysis while using the tissue characteristic parameter values. Theprocessing circuitry is configured to calculate a statistic value of thetissue characteristic parameter values in the measurement region.

Exemplary embodiments of an analyzing apparatus and an analyzingcomputer program will be explained with reference to the accompanyingdrawings. In the embodiments described below, an ultrasound diagnosisapparatus will be explained as an example of the analyzing apparatus.However, possible embodiments are not limited to this example. Forinstance, as the analyzing apparatus, other medical image diagnosisapparatuses besides ultrasound diagnosis apparatuses are alsoapplicable, such as X-ray diagnosis apparatuses, X-ray ComputedTomography (CT) apparatuses, Magnetic Resonance Imaging (MRI)apparatuses, Single Photon Emission Computed Tomography (SPECT)apparatuses, Positron Emission computed Tomography (PET) apparatuses,SPECT-CT apparatuses in which a SPECT apparatus and an X-ray CTapparatus are integrated together, PET-CT apparatuses in which a PETapparatus and an X-ray CT apparatus are integrated together, or a groupmade up of any of these apparatuses. Further, as the analyzingapparatus, not only medical image diagnosis apparatuses, but alsoarbitrary medical information processing apparatuses are applicable.

First Embodiment

FIG. 1 is a block diagram illustrating an exemplary configuration of anultrasound diagnosis apparatus 1 according to a first embodiment. Asillustrated in FIG. 1, the ultrasound diagnosis apparatus 1 according tothe first embodiment includes an apparatus main body 100, an ultrasoundprobe 101, an input device 102, and a display 103. The ultrasound probe101, the input device 102, and the display 103 are connected to theapparatus main body 100. In this situation, an examined subject(hereinafter, “patient”) P is not included in the configuration of theultrasound diagnosis apparatus 1.

The ultrasound probe 101 includes a plurality of transducer elements(e.g., piezoelectric transducer elements). Each of the plurality oftransducer elements is configured to generate an ultrasound wave on thebasis of a drive signal supplied thereto from transmitting and receivingcircuitry 110 (explained later) included in the apparatus main body 100.Further, the plurality of transducer elements included in the ultrasoundprobe 101 are configured to receive reflected waves from the patient Pand to convert the reflected waves into an electrical signal. Further,the ultrasound probe 101 includes matching layers provided for thetransducer elements, as well as a backing member or the like thatprevents the ultrasound waves from propagating rearward from thetransducer elements.

When an ultrasound wave is transmitted the ultrasound probe 101 to thepatient P, the transmitted ultrasound wave is repeatedly reflected on asurface of discontinuity of acoustic impedances at a tissue in the bodyof the patient P and is received as a reflected-wave signal (an echosignal) by each of the plurality of transducer elements included in theultrasound probe 101. The amplitude of the received reflected-wavesignal is dependent on the difference between the acoustic impedances onthe surface of discontinuity on which the ultrasound wave is reflected.When a transmitted ultrasound pulse is reflected on the surface of amoving blood flow, a cardiac wall, or the like, the reflected-wavesignal is, due to the Doppler effect, subject to a frequency shift,depending on a velocity component of the moving members with respect tothe ultrasound wave transmission direction.

The first embodiment is applicable to a situation where the ultrasoundprobe 101 illustrated in FIG. 1 is a one-dimensional ultrasound probe inwhich a plurality of piezoelectric transducer elements are arranged in arow, a one-dimensional ultrasound probe in which a plurality ofpiezoelectric transducer elements arranged in a row are mechanicallyswayed, or a two-dimensional ultrasound probe in which a plurality ofpiezoelectric transducer elements are two-dimensionally arranged in agrid formation.

The input device 102 includes a mouse, a keyboard, a button, a panelswitch, a touch command screen, a foot switch a trackball, a joystick,and/or the like. The input device 102 is configured to receive varioustypes of setting requests from an operator of the ultrasound diagnosisapparatus 1 and to transfer the received various types of settingrequests to the apparatus main body 100.

The display 103 is configured to display a Graphical User Interface(GUI) used by the operator of the ultrasound diagnosis apparatus 1 toinput the various types of setting requests through the input device 102and to display ultrasound image data generated by the apparatus mainbody 100 or the like.

The apparatus main body 100 is an apparatus configured to generate theultrasound image data on the basis of the reflected-wave signalsreceived by the ultrasound probe 101. As illustrated in FIG. 1, theapparatus main body 100 includes the transmitting and receivingcircuitry 110, signal processing circuitry 120, image processingcircuitry 130, an image memory 140, storage circuitry 150, andprocessing circuitry 160. The transmitting and receiving circuitry 110,the signal processing circuitry 120, the image processing circuitry 130,the image memory 140, the storage circuitry 150, and the processingcircuitry 160 are connected to one another so as to be able tocommunicate with one another.

The transmitting and receiving circuitry 110 includes a pulsergenerator, a transmission delay unit, a pulser, and the like and isconfigured to supply the drive signal to the ultrasound probe 101. Thepulse generator is configured to repeatedly generate a rate pulse usedfor forming transmission ultrasound wave, at a predetermined ratefrequency. Further, the transmission delay unit applies a delay periodthat is required to converge the ultrasound wave generated by theultrasound probe 101 into the form of a beam and to determinetransmission directionality and that corresponds to each of thepiezoelectric transducer elements, to each of the rate pulses generatedby the pulse generator. Further, the pulser applies the drive signal (adrive pulse) to the ultrasound probe 101 with timing based on the ratepulses. In other words, by varying the delay periods applied to the ratepulses, the transmission delay unit arbitrarily adjusts the transmissiondirections of the ultrasound waves transmitted from the surfaces of thepiezoelectric transducer element.

The transmitting and receiving circuitry 110 has a function to be ableinstantly change the transmission frequency, the transmission drivevoltage, and the like, for the purpose of executing a predeterminedscanning sequence on the basis of an instruction from the processingcircuitry 160 (explained later). In particular, the configuration tochange the transmission drive voltage is realized by using alinear-amplifier-type transmission circuitry of which the value can beinstantly switched or by using a mechanism configured to electricallyswitch between a plurality of power source units.

Further, the transmitting and receiving circuitry 110 includes apre-amplifier, an Analog/Digital (A/D) converter, a reception delayunit, an adder, and the like. The transmitting and receiving circuitry110 is configured to generate reflected-wave data by performing varioustypes of processes on the reflected-wave signals received by theultrasound probe 101. The pre-amplifier is configured to amplify thereflected-wave signal for each of the channels. The A/D converter isconfigured to apply an A/D conversion to the amplified reflected-wavesignals. The reception delay unit is configured to apply a delay periodrequired to determine the reception directionality. The adder isconfigured to generate the reflected-wave data by performing an addingprocess on the reflected-wave signals processed by the reception delayunit. As a result of the adding process performed by the adder,reflected components from the direction corresponding to the receptiondirectionality of the reflected-wave signals are emphasized, so that acomprehensive beam for transmitting and receiving the ultrasound wave isformed on the basis of the reception directionality and the transmissiondirectionality.

When a two-dimensional region of the patient P is to be scanned, thetransmitting and receiving circuitry 110 causes the ultrasound probe 101to transmit an ultrasound beam in two-dimensional directions. Further,the transmitting and receiving circuitry 110 generates two-dimensionalreflected-wave data from reflected-wave signals received by theultrasound probe 101. In contrast, when a three-dimensional region ofthe patient P is to be scanned, the transmitting and receiving circuitry110 causes the ultrasound probe 101 to transmit an ultrasound beam inthree-dimensional directions. Further, the transmitting and receivingcircuitry 110 generates three-dimensional reflected-wave data fromreflected-wave signals received by the ultrasound probe 101.

For example, the signal processing circuitry 120 is configured togenerate data (B-mode data) in which the signal intensity at eachsampling point is expressed as a level of brightness, by performing alogarithmic amplifying process, an envelope detecting process, or thelike on the reflected-wave data received from the transmitting andreceiving circuitry 110. The B-mode data generated by the signalprocessing circuitry 120 is output to the image processing circuitry130.

Further, for example, by using the reflected-wave data received from thetransmitting and receiving circuitry 110, the signal processingcircuitry 120 generates data (Doppler data) obtained by extractingmotion information based on the Doppler effect on moving members fromeach of the sampling points within a scanned region. More specifically,the signal processing circuitry 120 generates the data (the Dopplerdata) obtained by performing a frequency analysis on the reflected-wavedata to acquire velocity information, extracting blood flows, tissues,and contrast-agent echo components subject to the Doppler effect, andextracting moving member information such as an average velocity, avariance value, a power value, and the like from multiple points. Inthis situation, the moving members may be, for example, blood flows,tissues such as cardiac walls, a contrast agent, and the like. Themotion information (blood flow information) obtained by the signalprocessing circuitry 120 is sent to the image processing circuitry 130and is displayed on the display 103 in color as an average velocityimage, a variance image, a power image, or an image combining any ofthese images.

Further, as illustrated in FIG. 1, the signal processing circuitry 120executes an analyzing function 121. In this situation, for example,processing functions executed by the analyzing function 121, which is aconstituent element of the signal processing circuitry 120 illustratedin FIG. 1, are recorded in a storage device (e.g., the storage circuitry150) of the ultrasound diagnosis apparatus 1 in the form of acomputer-executable program. The signal processing circuitry 120 is aprocessor configured to realize functions corresponding to computerprograms (hereinafter, “programs”), by reading the programs from thestorage device and executing the read programs. In other words, thesignal processing circuitry 120 that has read the programs has thefunctions illustrated within the signal processing circuitry 120 inFIG. 1. The processing functions executed by the analyzing function 121will be explained later.

The image processing circuitry 130 is configured to generate ultrasoundimage data from the data generated by the signal processing circuitry120. From the B-mode data generated by the signal processing circuitry120, the image processing circuitry 130 is configured to generate B-modeimage data in which intensities of the reflected waves are expressedwith levels of brightness. Further, from the Doppler data generated bythe signal processing circuitry 120, the image processing circuitry 130is configured to generate Doppler image data expressing the movingmember information. The Doppler image data may be velocity image data,variance image data, power image data, or image data combining any ofthese types of image data.

In this situation, generally speaking, the image processing circuitry130 converts (by performing a scan convert process) a scanning linesignal sequence from an ultrasound scan into a scanning line signalsequence in a video format used by, for example, television andgenerates display-purpose ultrasound image data. More specifically, theimage processing circuitry 130 generates the display-purpose ultrasoundimage data by performing a coordinate transformation process compliantwith the ultrasound scanning mode used by the ultrasound probe 101.Further, as various types of image processing processes other than thescan convert process, the image processing circuitry 130 performs, forexample, an image processing process (called a smoothing process) tore-generate an average brightness value image by using a plurality ofimage frames resulting from the scan convert process and an imageprocessing process (called an edge enhancement process) performed byusing a differential filter within an image. Further, the imageprocessing circuitry 130 combines ultrasound image data with additionalinformation (e.g., text information of various parameters, scalegraduations, body marks, and/or the like).

In other words, the B-mode data and the Doppler data are each ultrasoundimage data before the scan convert process is performed. In contrast,the data generated by the image processing circuitry 130 is thedisplay-purpose ultrasound image data after the scan convert process isperformed. In this situation, when the signal processing circuitry 120has generated three-dimensional data (three-dimensional B-mode data andthree-dimensional Doppler data), the image processing circuitry 130generates volume data by performing a coordinate transformation processcorresponding to the ultrasound scanning mode used by the ultrasoundprobe 101. After that, the image processing circuitry 130 generatesdisplay-purpose two-dimensional image data by performing various typesof rendering processes on the volume data.

The image memory 140 is a memory configured to store therein thedisplay-purpose image data generated by the image processing circuitry130. Further, the image memory 140 is also capable of storing thereinthe data generated by the signal processing circuitry 120. The B-modedata and the Doppler data stored in the image memory 140 may be, forexample, invoked by the operator after a diagnosis procedure and mayserve as display-purpose ultrasound image data after being routedthrough the image processing circuitry 130.

The storage circuitry 150 is configured to store therein a controlcomputer program (hereinafter “control program”) used for performing anultrasound transmission/reception, image processing processes, anddisplaying processes, as well as various types of data such as diagnosisinformation (e.g., patients' IDs, observations of medical doctors,etc.), diagnosis protocols, various types of body marks, and the like.Further, the storage circuitry 150 may also be used for storing thereinany of the image data stored in the image memory 140, as necessary.Further, it is also possible to transfer any of the data stored in thestorage circuitry 150 to an external apparatus via an interface (notillustrated).

The processing circuitry 160 is configured to control the overallprocessing of the ultrasound diagnosis apparatus 1. More specifically,the processing circuitry 160 is configured to control processesperformed by the transmitting and receiving circuitry 110, the signalprocessing circuitry 120, and the image processing circuitry 130, on thebasis of the various types of setting requests input by the operator viathe input device 102 and various types of control programs and varioustypes of data read from the storage circuitry 150. Further, theprocessing circuitry 160 exercises control so that the display 103displays the display-purpose ultrasound image data stored in the imagememory 140.

Further, as illustrated in FIG. 1, the processing circuitry 160 includesan index value calculating function 161, a determining function 162, astatistic value calculating function 163, and a display controllingfunction 164. In this situation, for example, processing functionsexecuted by the index value calculating function 161, the determiningfunction 162, the statistic value calculating function 163, and thedisplay controlling function 164, which are constituent elements of theprocessing circuitry 160 illustrated in FIG. 1, are recorded in astorage device (e.g., the storage circuitry 150) of the ultrasounddiagnosis apparatus 1 in the form of computer-executable programs. Theprocessing circuitry 160 is a processor configured to realize thefunctions corresponding to the programs, by reading the programs fromthe storage device and executing the read programs. In other words, theprocessing circuitry 160 that has read the programs has the functionsillustrated within the processing circuitry 160 in FIG. 1. Theprocessing functions executed by the index value calculating function161, the determining function 162, the statistic value calculatingfunction 163, and the display controlling function 164 will be explainedlater.

The term “processor (or circuit)” used in the above explanation denotes,for example, a Central Processing Unit (CPU), a Graphics Processing Unit(GPU), or a circuit such as an Application Specific Integrated Circuit(ASIC) or a programmable logic device (e.g., a Simple Programmable LogicDevice [SPLD], a Complex Programmable Logic Device [CPLD], or a FieldProgrammable Gate Array [FPGA]). The processors each realize thefunctions by reading and executing the programs stored in the storagecircuitry 150. It is also acceptable to directly incorporate theprograms into the circuits of the processors, instead of storing theprograms in the storage circuitry 150. In that situation, the processorseach realize the functions by reading and executing the programsincorporated in the circuit thereof. Further, as for the processorsaccording to the first embodiment, each of the processors may bestructured as a single circuit. Alternatively, it is also acceptable torealize the functions thereof by structuring a single processor bycombining together a plurality of independent circuits. Further, it isalso acceptable to integrate the plurality of constituent elementsillustrated in each of the drawings into one processor so as to realizethe functions thereof.

The ultrasound diagnosis apparatus 1 according to the first embodimentis an apparatus capable of implementing elastography by measuringfirmness levels of a tissue in a patient's body and expressing adistribution of the measured firmness levels in an image. Morespecifically, the ultrasound diagnosis apparatus 1 according to thefirst embodiment is an apparatus capable of implementing elastography bycausing a displacement in the tissue in the patient's body by applyingan acoustic radiation force thereto.

In other words, the transmitting and receiving circuitry 110 accordingto the first embodiment arranges the ultrasound probe 101 to transmit apush pulse that causes the displacement in the tissue in the patient'sbody on the basis of the acoustic radiation force. Further, thetransmitting and receiving circuitry 110 according to the firstembodiment further arranges the ultrasound probe 101 to transmit anobservation-purpose pulse used for observing the displacement in thetissue in the patient's body caused on the basis of the push pulse. Theobservation-purpose pulse is transmitted for the purpose of observing,at each of the sampling points in a scanned region, shear velocity of atransverse wave called a shear wave caused by the push pulse. Usually,the observation-purpose pulse is transmitted multiple times (e.g., 100times) to each of the scanning lines within the scanned region. Thetransmitting and receiving circuitry 110 generates reflected-wave datafrom reflected-wave signals of the observation-purpose pulse transmittedwith respect to each of the scanning lines within the scanned region. Inthis situation, the scanned region that is scanned by theobservation-purpose pulse corresponds to a region (which may hereinafterbe referred to as a “display Region Of Interest (ROI)”) in which thefirmness levels of the tissue in the patient's body are displayed byusing elastography.

Further, in the signal processing circuitry 120, the analyzing function121 calculates firmness distribution data indicating a distribution offirmness levels in the display ROI, by analyzing the reflected-wave dataof the observation-purpose pulse that was transmitted multiple timeswith respect to each of the scanning lines within the display ROI. Morespecifically, the analyzing function 121 generates the firmnessdistribution data of the display ROI, by measuring, at each of thesampling points, the shear velocity of the shear wave caused by the pushpulse. In other words, the analyzing function 121 calculates shearvelocity values serving as tissue characteristic parameter values, byanalyzing a result of the scan performed on the patient P. In thissituation, the analyzing function 121 is an example of an analyzingunit. In other words, the analyzing function 121 calculates the tissuecharacteristic parameter value with respect to each of the plurality ofpositions within the region of interest, by analyzing the result of thescan performed on the patient.

For example, the analyzing function 121 performs a frequency analysis onthe reflected-wave data of the observation-purpose pulse. Accordingly,the analyzing function 121 generates motion information (tissue Dopplerdata) corresponding to a plurality of temporal phases, at each of theplurality of sampling points on the scanning lines. Further, theanalyzing function 121 integrates with respect time the velocitycomponents of the tissue Doppler data corresponding to the plurality oftemporal phases and having been obtained at each of the plurality ofsampling points on the scanning lines. Accordingly, the analyzingfunction 121 calculates a displacement corresponding to the plurality oftemporal phases at each of the plurality of sampling points on thescanning lines. Subsequently, the analyzing function 121 obtains a timehaving the largest displacement at each of the sampling points. Afterthat, the analyzing function 121 determines the time having the largestdisplacement at each of the sampling points, as an arrival time at whichthe shear wave arrived at the sampling point. Subsequently, theanalyzing function 122 calculates the shear velocity value of the shearwave at each of the sampling points, by spatially differentiating thearrival time of the shear wave at each of the sampling points. In thissituation, as the arrival time of the shear wave, it is also acceptableto use, for example, a time having the largest amount of change in thedisplacement at each of the sampling points, instead of the time havingthe largest displacement at each of the sampling points.

Further, as the firmness distribution data, the analyzing function 121generates information about the shear velocity value of the shear waveat each of the sampling points within the display ROI. Firmer tissuesexhibit higher shear velocity of the shear wave. On the contrary, softertissues exhibit lower shear velocity of the shear wave. In other words,shear velocity values of the shear wave serve as values indicatinglevels of firmness (moduli of elasticity) of the tissue. In the exampleabove, the observation-purpose pulse is a transmission pulse for tissueDoppler. Alternatively, for example, it is also acceptable for theanalyzing function 121 to calculate the shear velocity value of theshear wave by detecting the shear velocity from a cross-correlation oftissue displacements between adjacently-positioned scanning lines,instead of the calculation based on the time (the arrival time) havingthe largest displacement at each of the sampling points.

In another example, the analyzing function 121 may calculate a modulusof elasticity (or a Young's modulus or a shearing modulus of elasticity)from the shear velocity and may further generate firmness distributiondata by using the calculated modulus of elasticity. It is possible touse the shear velocity, the Young's modulus, and the shearing modulus ofelasticity, each as a physical quantity (an index value) indicating alevel of firmness of a tissue in a patient's body.

Further, the image processing circuitry 130 generates firmness imagedata, by assigning, to each of different positions in the display ROI, apicture value corresponding to the shear velocity value at each of thesampling points of the firmness distribution data. The firmness imagedata generated by the image processing circuitry 130 is displayed by thedisplay 103 as a firmness image, while being superimposed on a B-modeimage, for example. In this situation, the firmness image is an imagebased on the shear velocity values and is an example of an image basedon tissue characteristic parameter values.

An example of a configuration of the ultrasound diagnosis apparatus 1according to the first embodiment has thus been explained. Theultrasound diagnosis apparatus 1 according to the first embodimentconfigured as described above performs processes described below for thepurpose of analyzing a characteristic of a tissue with an excellentlevel of precision. In other words, the processing circuitry 160according to the first embodiment executes the index value calculatingfunction 161, the determining function 162, the statistic valuecalculating function 163, and the display controlling function 164.

In the embodiments described below, an example will be explained inwhich the “shear velocity values” indicating a level of firmness of atissue in the patient's body is used as a parameter expressing acharacteristic of the tissue (which may be referred to as a “tissuecharacteristic parameter”). However, possible embodiments are notlimited to this example. It is possible to apply an arbitrary tissuecharacteristic parameter. Other tissue characteristic parameters will beexplained later.

The index value calculating function 161 is configured to calculate anindex value related to variance among the tissue characteristicparameter values. For example, the index value calculating function 161calculates an index value for each of a plurality of sub-regionsincluded in a region where a scan was performed. The index valuecalculating function 161 is an example of an index value calculatingunit. In other word the index value calculating function 161 calculatesthe index value related to the variance among the tissue characteristicparameter values with respect to each of the plurality of sub-regionsincluded in the region of interest.

FIG. 2 is a drawing for explaining a process performed by the indexvalue calculating function 161 according to the first embodiment. Theleft section of FIG. 2 illustrates a display ROI expressed in an imageby elastography. Further, the right section of FIG. 2 illustratessampling positions in raw data (prior to the scan conversion)corresponding to the display ROI in the left section of FIG. 2. In theright section of FIG. 2, the horizontal direction (the azimuthdirection) corresponds to the number of beams in the display ROI,whereas the vertical direction (the depth direction) corresponds to thenumber of samples received with each beam.

As illustrated in FIG. 2, the index value calculating function 161divides a firmness image corresponding to the display ROI into aplurality of sub-regions. More specifically, the index value calculatingfunction 161 divides the azimuth direction of the display ROI intosections at predetermined intervals corresponding to the number of beamsand divides the depth direction of the display ROI into sections atpredetermined intervals corresponding to the number of samples (theright section of FIG. 2). Each of the regions (hereinafter, “segmentedregions”) resulting from the dividing performed by the index valuecalculating function 161 includes a plurality of sampling points. Thesegmented regions serve as an example of sub-regions.

Further, the index value calculating function 161 calculates a varianceamong the shear velocity values each of the segmented regions. Forexample, the index value calculating function 161 calculates, for eachof the segmented regions, the variance among the shear velocity values,by using the shear velocity value at each of the plurality of samplingpoints included in the segmented region.

In this manner, the index value calculating function 161 calculates, foreach of the segmented regions, the variance among the shear velocityvalues, as an index value related to the variance among the tissuecharacteristic parameter values. The explanation above of the indexvalue calculating function 161 is merely an example. Possibleembodiments are not limited to this example. For instance, not only thevariance value, the index value calculating function 161 may calculate astandard deviation or a residual sum of squares, as an index value.

Further, in the description above, the example is explained in which thesegmented regions obtained by dividing the firm image are used as thesub-regions; however, possible embodiments are not limited to thisexample. For instance, the sub-regions for each of which the index valueis calculated may be regions that each have an arbitrary shape and arepositioned in the firmness image in a discrete manner.

The determining function 162 is configured to determine a measurementregion on the basis of the index value. For example, the determiningfunction 162 determines the measurement region on the basis of acomparison between the index value of each of the plurality ofsub-regions and a threshold value. In the following sections, themeasurement region may also be referred to as a “measurement ROI”. Thedetermining function 162 is an example of a determining unit. In otherwords, the determining function 162 determines the measurement region inthe region of interest, by performing the analysis while using thetissue characteristic parameter values. Further, the determiningfunction 162 determines the measurement region by comparing the indexvalue of each of the plurality of sub-regions with the threshold value.

FIGS. 3, 4, and 5 are drawings for explaining a process performed by thedetermining function 162 according to the first embodiment. In FIG. 3,the vertical direction corresponds to the index value (the varianceamong the shear velocity values), whereas the horizontal directioncorresponds to arbitrary segmented regions.

As illustrated in FIG. 3, by comparing the variance among the shearvelocity values of each of the segmented regions with the thresholdvalue, the determining function 162 judges whether or not the varianceamong the shear velocity values is equal to or larger than the thresholdvalue. In the example illustrated in FIG. 3, the segmented regions ofwhich the variance among the shear velocity values is equal to or largerthan the threshold value are indicated with white dots, whereas thesegmented regions of which the variance among the shear velocity valuesis smaller than the thresh value are indicated with black dots. In otherwords, for each of the segmented regions, the determining function 162judges whether the variance among the shear velocity values of thesegmented region is equal to or larger than the threshold value (a whitedot) or smaller than the threshold value (a black dot). In thissituation, the threshold value used for the comparison with the variancevalues is a value by which it is possible to determine that the varianceamong the tissue characteristic parameter values within the segmentedregion is large. The threshold value is registered by the operator inadvance on the basis of reference values from the past. In other words,such a segmented region of which the variance value is equal to orlarger than the threshold value is determined to contain noise.

Further, as illustrated in FIG. 4, for example, the determining function162 generates an SD map on the basis of the result of the comparisonbetween the index value (the variance values) and the threshold value.In the present example, the SD map is information in which the result ofthe comparison between the variance of each of the segmented regions andthe threshold value is indicated in a corresponding position within theraw data. In the example illustrated in FIG. 4, the segmented regions ofwhich the variance is equal to or larger than the threshold value areindicated with “white dots”, whereas the segmented regions of which thevariance is smaller than the threshold value are indicated with “blackdots”. In other words, the determining function 162 generates the SD mapindicating such a region of which the tissue characteristic parametervalue is determined to be noise with a “white dot” and indicating such aregion of which the tissue characteristic parameter value is determinednot to be noise with a “black dot”.

After that, the determining function 162 determines the measurement ROIon the basis of the SD map. For example, the determining function 162determines at least one measurement ROI from within a region combiningtogether such segmented regions each determined to have small variance.In other words, the determining function 162 rejects (will not adopt)such segmented regions each determined to have large variance andindicated with a “white dot” and further determines the measurement ROIfrom among such segmented regions each determined to have small variancevalue and indicated with a “black dot”.

In this situation, the determining function 162 determines themeasurement ROI according to information (rules) set in advance. Forexample, the determining function 162 may determine a region having ashape and a size that are set in advance as a measurement ROI. Also, thedetermining function 162 may determine regions of which the quantity isset in advance, as measurement ROIs.

With reference to FIG. 5, an example will be explained in which thedetermining function 162 determines a measurement ROI according to therule where “all the segmented regions each having small variance aredetermined to form a measurement ROI”. In that situation, thedetermining function 162 determines a region combining together all thesegmented regions indicated with the “black dots” in the SD map as ameasurement ROI (R1) (the left section of FIG. 5). For example, thedetermined measurement ROI (R1) is displayed over the display ROI by thedisplay controlling function 164 (explained later) (the right section ofFIG. 5).

In this manner, the determining function 162 determines the measurementregion on the basis of the variance among the shear velocity values ineach of the segmented regions. The above explanation about thedetermining function 162 is merely an example. Possible embodiments arenot limited to this example. For instance, the coordinate system in FIG.3 as well as the raw data and the SD map in FIGS. 4 and 5 areillustrated for the sake of convenience in the explanation. These piecesof information do not necessarily have to be displayed on the display103. Further, not only the variance value, the determining function 162may calculate a standard deviation or a residual sum of squares, as anindex value.

Further, although FIG. 4 illustrates the example in which the SD map isgenerated based on the raw data before the scan conversion is performed,possible embodiments are not limited to this example. For instance, ifis also acceptable to generate an SD map based on the image data afterthe scan conversion (i.e., an image corresponding to the display ROI).Because the sampling positions within the raw data and pixel positionswithin the image data are kept in correspondence with one another, it ispossible to perform the process similarly regardless of whether the datais from before the scan conversion or from after the scan conversion.The scan conversion in this situation does not need to include aninterpolation process between the scanning lines.

Further, although FIG. 5 illustrates the example in which themeasurement ROI is determined based on the raw data before the scanconversion, possible embodiments are not limited to this example. Forinstance, it is also acceptable to determine a measurement ROI based onthe image data after the scan conversion.

Further, although FIG. 5 illustrates the example in which the singlemeasurement ROI is determined, possible embodiments are not limited tothis example. For instance, it is acceptable to determine an arbitrarynumber of measurement ROIs. Further, besides the rule where “all thesegmented regions each having small variance are determined to form ameasurement ROI”, for example, it is acceptable to determine ameasurement ROI by using any of other various rules. For example, it isalso acceptable to determine a measurement ROI according to a ruledefining an arbitrary shape and an arbitrary size such as “a circularregion having the largest diameter (the largest inscribed circle)extracted from within a region combining together all the segmentedregions each having small variance, is determined as a measured ROI”.Alternatively, for example, it is also acceptable to determine ameasurement ROI according to a rule based on a variance value such as “aregion that has a predetermined shape and in which an average of thevariance values is the smallest is determined as a measurement ROI”.Further, the outline of the measurement ROI (R1) in FIG. 5 does notnecessarily have to be displayed. An arrangement is also acceptable inwhich the operator is able to switch between displaying and notdisplaying of the outline, as appropriate.

When the shape of the measurement ROI is defined by using the rulesdescribed above, it is desirable that the shape is defined as a shape tobe displayed with the display ROI. The reason is that, for example, asignal sequence from the scanning lines (i.e., the raw data) acquired byperforming a sector scan does not match the coordinate system of thedisplay image. In other words, for example, if the shape was definedwithin the raw data acquired through a sector scan, the defined shapewould be changed when the signal is converted into the coordinate systemof the display image by the scan conversion. For this reason, when theshape of the measurement ROI is defined, it is desirable that the shapeis defined as a shape to be displayed with the display ROI. For example,when a “circular” shape is defined as a shape to be displayed with thedisplay ROI, it is possible to display a “circular” measurement ROI overthe display ROI, both before the scan conversion and after theconversion. More specifically, when the process is performed with theimage data after the scan conversion, it is possible to set a “circular”measurement ROI, by applying the defined “circular” shape without anymodification. Alternatively, when the process is performed with the rawdata before the scan conversion, it is possible to set a “circular”measurement ROI, by deforming a “circular” shape (called “reversedeformation”) into a shape corresponding to the signal sequence from thescanning lines and applying the deformed shape.

The statistic value calculating function 163 is configured to calculatea statistic value of the tissue characteristic parameter values in themeasurement region. For example, the statistic value calculatingfunction 163 calculates an average of the shear velocity values in themeasurement ROI. The statistic value calculating function 163 is anexample of a statistic value calculating unit. In other words, thestatistic value calculating function 163 calculates the statistic valueof the tissue characteristic parameter values in the measurement region.

In the example in FIG. 5, the statistic value calculating function 163calculates an average of the shear velocity values, by using the shearvelocity value at each of the plurality of sampling points included inthe measurement ROI (R1). When a plurality of measurement ROIs aredetermined, the statistic value calculating function 163 calculates anaverage of the shear velocity values for each of the measurement ROIs.

In this manner, the statistic value calculating function 163 calculatesthe statistic value of the tissue characteristic, parameter values inthe measurement region. Although the average value is calculated as thestatistic value in the above example, possible embodiments are notlimited to this example. Depending on analyses to be performed, it ispossible to calculate any arbitrary statistic value such as a median, avariance value, a standard deviation, a residual sum of squares, and/orthe like.

The display controlling function 164 is configured to display themeasurement region over an image based on the tissue characteristicparameter values. Further, for example, the display controlling function164 is configured to cause the display 103 to display the statisticvalue calculated by the statistic value calculating function 163. Thedisplay controlling function 164 is an example of a display controllingunit.

For example, as illustrated in FIG. 5, the display controlling function164 arranges the measurement ROI (R1) to be displayed over the displayROI. Further, the display controlling function 164 arranges the averageof the shear velocity values in the measurement ROI (R1) calculated bythe statistic value calculating function 163 to be displayed incorrespondence with the measurement ROI (R1).

Further, for example, the display controlling function 164 may arrangethe sub-regions to be displayed over an image based on the tissuecharacteristic parameter values. For example, the display controllingfunction 164 may arrange the plurality of segmented regions indicated inthe SD map in FIG. 5 to be displayed over the display ROI.

FIG. 6 is a flowchart illustrating a processing procedure performed bythe ultrasound diagnosis apparatus 1 according to the first embodiment.The processing procedure illustrated in FIG. 6 is started when, forexample, the operator instructs that a measuring process be startedwhile the display 103 is displaying a firmness image subject to themeasuring process.

As illustrated in FIG. 6, when an instruction indicating that ameasuring process should be started is received (step S101: Yes), theprocessing circuitry 160 starts the process at step) S102 andthereafter. Unless the processing circuitry 160 receives the instructionindicating that a measuring process should be started (step S101: No),the processing circuitry 160 is in a standby state.

Subsequently, the index value calculating function 161 divides thefirmness image into a plurality of segmented regions (step S102). Forexample, the index value calculating function 161 divides the azimuthdirection of the display ROI at predetermined intervals corresponding tothe number of beams and divides the depth direction of the display ROIat predetermined intervals corresponding to the number of samples.

Further, the index value calculating function 161 calculates a varianceamong the shear velocity values for each of the segmented regions (stepS103). For example, the index value calculating function 161 calculates,for each of the segmented regions, a variance among the shear velocityvalues, by using the shear velocity value at each of the plurality ofsampling points included in each of the segmented regions.

After that, the determining function 162 compares the variance among theshear velocity values in each of the segmented regions with thethreshold value (step S104). For example, the determining function 162compares, for each of the segmented regions, the variance among theshear velocity values with the threshold value and judges whether or notthe variance among the shear velocity values is equal to or larger thanthe threshold value. After that, the determining function 162 generatesan SD map on the basis of results of the comparison between thevariances and the threshold values.

Subsequently, the determining function 162 determines a measurement ROIon the basis of the comparison results (step S105). For example, thedetermining function 162 determines the measurement ROI (R1) on thebasis of the SD map. More specifically, the determining function 162determines either a region having a predetermined shape and apredetermined size or regions of which the quantity is a predeterminedvalue, as one or more measurement ROIs, according to the information(the rule) set in advance.

After that, the display controlling function 164 causes the determinedmeasurement ROI to be displayed over the firmness image (step S106). Forexample, the display controlling function 164 causes the determinedmeasurement ROI (R1) to be displayed over a display ROI of the firmnessimage.

Subsequently, the statistic value calculating function 163 calculates anaverage of the shear velocity values in the measurement ROI (step S107).For example, the statistic value calculating function 163 calculates anaverage of the shear velocity values, by using the shear velocity valueat each of the plurality of sampling points included in the measurementROI (R1).

After that, the display controlling function 164 causes the average ofthe shear velocity values to be displayed (step S108). For example, thedisplay controlling function 164 causes the average of the shearvelocity values in the measurement ROI (R1) calculated by the statisticvalue calculating function 163 to be displayed in correspondence withthe measurement ROI (R1).

The processing procedure in FIG. 6 explained above is merely an example.Possible embodiments are not limited to this example. For instance, theprocessing procedure in FIG. 6 does not necessarily have to be performedin the order described above. For example, the process of displaying themeasurement ROI (step S106) may be performed at the same time as theprocess of displaying the average of the shear velocity values (stepS108). Further, for example, the process of displaying the measurementROI (step S106) does not necessarily have to be performed. In otherwords, it is sufficient as long as the measurement results from thedetermined measurement ROI are displayed, even without having themeasurement ROI displayed.

As explained above, in the ultrasound diagnosis apparatus 1 according tothe first embodiment, the analyzing function 121 is configured tocalculate the tissue characteristic parameter values by analyzing theresult of the scan performed on the patient P. After that, the indexvalue calculating function 161 is configured to calculate the indexvalue related to the variance among the tissue characteristic parametervalues. Subsequently, the determining function 162 is configured todetermine the measurement region on the basis of the index value. Thestatistic value calculating function 163 is configured to calculate thestatistic value of the tissue characteristic parameter values in themeasurement region. With these arrangements, the ultrasound diagnosisapparatus 1 makes it possible to analyze the tissue characteristic withan excellent level of precision.

FIG. 7 is a drawing for explaining advantageous effects achieved by theultrasound diagnosis apparatus 1 according to the first embodiment. FIG.7 illustrates an example in which two circular measurement ROIs are setin a firmness image corresponding to a display ROI within a B-modeimage.

In the display ROI in FIG. 7, although the firmness image havingsubstantially uniform firmness levels (shear velocity) is displayed,there are a number of locations where the levels of firmness arepartially different. Such regions represent, for example, noise causedby impacts from nearby structures or blood vessels. It is consideredthat such regions do not indicate the actual firmness levels of thetissue (a tissue characteristic). In this situation, when measurementROIs are manually designated, there is a possibility that themeasurement ROIs may contain noise. In the example in FIG. 7, themeasurement ROI on the left-hand side contains noise. For this reason,if a measuring process were performed by using the measurement ROIpositioned on the left, results of the measuring process (e.g., anaverage of firmness levels) would also contain noise.

To cope with this situation, the ultrasound diagnosis apparatus 1according to the first embodiment is configured, when performing ameasuring process in a firmness image, to calculate a variance amongshear velocity values, serving as tissue characteristic parameter valuesindicating levels of firmness, with respect to each of the sub-regionsand to further set measurement ROIs each in a region having smallvariance among the shear velocity values. With this configuration, forexample, the ultrasound diagnosis apparatus 1 is able to set regionscontaining no noise as the measurement ROIs, as indicated by themeasurement ROIs illustrated in the right section of FIG. 7. Theultrasound diagnosis apparatus 1 thus makes it possible to analyze thetissue characteristic with an excellent level of precision.

Further, when performing a measuring process by using a firmness imagebased on the shear velocity values of the shear wave, the ultrasounddiagnosis apparatus 1 according to the first embodiment determines oneor more measurement ROIs by directly evaluating the shear velocityvalues themselves, which are subject to the measuring process. With thisconfiguration, the ultrasound diagnosis apparatus 1 is able todetermine, as the measurement ROIs, stable regions that each have smallvariance among the tissue characteristic parameter values, which aresubject to the measuring process.

Further, the ultrasound diagnosis apparatus 1 according to the firstembodiment automatically determines the measurement ROIs by performingthe process described above. Consequently, the ultrasound diagnosisapparatus 1 is able to set the appropriate measurement ROIs with asimple operation, without requiring the operator to perform complicatedoperations.

Second Embodiment

In the first embodiment, the example is explained in which theultrasound diagnosis apparatus 1 automatically determines themeasurement ROIs. However, possible embodiments are not limited to thisexample. For instance, another arrangement is also acceptable in whichthe ultrasound diagnosis apparatus 1 is configured to determine one ormore measurement candidate regions (which may be referred to as“measurement candidate ROIs”) serving as candidates for a measurementROI and to further determine a region selected by the operator frozeamong the determined measurement candidate ROIs as a measurement ROI.

The ultrasound diagnosis apparatus 1 according to the second embodimenthas a similar configuration to that of the ultrasound diagnosisapparatus 1 illustrated in FIG. 1 where a part of the process performedby the determining function 162 is different. The second embodimenttherefore will be explained by placing a focus on differences from thefirst embodiment. Explanations of some of the configurations having thesame functions as those explained in the first embodiment will beomitted.

The determining function 162 is configured to determine measurementcandidate ROIs on the basis of a comparison between index values and athreshold value. For example, the determining function 162 determines atleast one measurement candidate region on the basis of a comparisonbetween an index value of each of the plurality of sub-regions and thethreshold value. After that, the determining function 162 determines ameasurement region from among said at least one measurement candidateregion.

For example, the determining function 162 determines a measurement ROIon the basis of an SD map. For example, the determining function 162determines at least one measurement candidate ROI from within a regioncombining together such segmented regions each determined to nave smallvariance.

In this situation, the determining function 162 determines themeasurement candidate ROI according to information (a rule) set inadvance. For example, the determining function 162 determines ameasurement candidate ROI having a shape and a size set in advance.Also, the determining function 162 determines one or more measurementcandidate ROIs of which the quantity is set in advance.

FIGS. 8A and 8B are drawings for explaining a process performed by thedetermining function 162 according to the second embodiment. Forexample, the determining function 162 determines one or more measurementcandidate ROIs according to the rules illustrated in FIGS. 8A and 8B.

The example in FIG. 8A illustrates an example in which the determiningfunction 162 determines measurement candidate ROIs and furtherdetermines a measurement ROI from among the measurement candidate ROIsaccording to the rule where “a square region made up of segmentedregions each having small variance that are arranged in a 2 by 2 orlarger formation is selected as a measurement candidate ROI”. In thissituation, from the region combining together the segmented regionsindicated with the “black dots” in the SD map, the determining function162 extracts square regions each made up of segmented regions arrangedin a 2 by 2 or larger formation and determines the extracted regions asmeasurement candidate ROIs. In the left section of FIG. 8A, thedetermining function 162 determines a measurement candidate ROI (R2), ameasurement candidate ROI (R3), a measurement candidate ROI (R4), ameasurement candidate ROI (R5), and a measurement candidate ROI (R6).The determined measurement candidate ROIs are displayed over the displayROI by the display controlling function 164 (the middle section of FIG.8A) After that, the determining function 162 receives, from theoperator, an operation to select a measurement ROI from among thedetermined measurement candidate ROIs. For example, when having receivedfrom the operator an operation to select the measurement candidate ROI(R6) as a measurement ROI, the determining function 162 determines themeasurement candidate ROI (R6) to be a measurement ROI (R6). In thatsituation, for example, the measurement candidate ROIs other than themeasurement ROI (R6) are brought into a non-display state, so that onlythe measurement ROI (R6) is displayed (the right section of FIG. 8A).

The example in FIG. 8B illustrates an example in which, according to therule where “all the segmented regions each having small variance aredetermined to form a measurement candidate ROI”, the determiningfunction 162 determines a measurement candidate ROI and furtherdetermines a measurement ROI having an arbitrary shape within themeasurement candidate ROI. In that situation, the determining function162 determines a region combining together all the segmented regionsindicated with the “black dots” in the SD map as a measurement candidateROI (R7) (the left section of FIG. 8B). For example, the determinedmeasurement candidate ROI (R7) is displayed over the display ROI by thedisplay controlling function 164 (the middle section of FIG. 8B). Afterthat, from the operator, the determining function 162 receives anoperation to designate a measurement ROI having an arbitrary shape fromwithin the area of the measurement candidate ROI (R7). For example, theoperator performs an operation of designating a circular region (R8) asthe measurement ROI, from within the area indicated with the outline ofthe measurement candidate ROI (R7) displayed over the display ROI (themiddle section of FIG. 8B). When having received the operation, thedetermining function 162 determines the region (R8) as a measurement ROI(R8). In that situation, for example, the measurement candidate ROI (R7)is brought into a non-display state, so that only the measurement ROI(R8) is displayed (the right section of FIG. 8B).

As explained above, the determining function 162 determines the one ormore measurement candidate regions on the basis of the variance valueamong the shear velocity values in each of the segmented regions andfurther determines the measurement region from among or from within theone or more determined measurement candidate regions. The abovedescription of the determining function 162 is merely an example.Possible embodiments are not limited to this example. For instance,although the example is explained above in which the plurality ofmeasurement candidates ROIs are determined, possible embodiments are notlimited to this example. For instance, a single measurement candidateROI may be determined.

Further, for example, FIG. 8A illustrates the example in which theoperator selects the measurement ROI from among the plurality ofmeasurement candidates ROIs. However, possible embodiments are notlimited to this example. For instance, the determining function 162 mayautomatically select a measurement ROI from among the plurality ofmeasurement candidate ROIs. For example, the determining function 162may determine a measurement ROI according to the rule where “the largestregion of a plurality of measurement candidate ROIs is determined as ameasurement ROI”. In that situation, the determining function 162determines the measurement candidate ROI (R6) as a measurement ROI (R6).In another example, the determining function 162 may determine ameasurement ROI according to the rule where “a region having thesmallest variance value among a plurality of measurement candidates ROIsis determined as a measurement ROI”. In that situation, the determiningfunction 162 calculates a variance value of each of the measurementcandidates ROI and determines one of the measurement candidate ROIshaving the smallest variance value as a measurement ROI.

FIG. 9 is a flowchart illustrating a processing procedure performed bythe ultrasound diagnosis apparatus according to the second embodiment.For example, the processing procedure in FIG. 9 is started when theoperator instructs that a measuring process should be started while afirmness image subject to the measuring process is being displayed onthe display 103. Of the processing procedure in FIG. 9, because theprocesses at steps S201 through S204 are the same as the processes atsteps S101 through S104 in FIG. 6, explanations thereof will be omitted.

As illustrated in FIG. 6, the determining function 162 determines aplurality of measurement candidate ROIs on the basis of the results ofthe comparison (step S205). For example, the determining function 162determines a plurality of measurement candidate ROIs on the basis of theSD map. More specifically, according to information (a rule) set inadvance, the determining function 162 determines either regions eachhaving a predetermined shape and a predetermined size or regions ofwhich the quantity is a predetermined value, as the measurementcandidates ROIs.

After that, the display controlling function 164 causes the determinedplurality of measurement candidate ROIs to be displayed over thefirmness image (step S206). For example, the display controllingfunction 164 causes the determined plurality of measurement candidatesROIs to be displayed over a display ROI.

Subsequently, the determining function 162 receives a selection of ameasurement ROI (step S207). For example, the determining function 162receives, from the operator, an operation to select the measurement ROIfrom among the plurality of measurement candidate ROIs displayed overthe display ROI. When having received the operation (step 207: Yes), thedetermining function 162 determines the measurement candidate ROIselected by the operation as a measurement ROI. Unless the determiningfunction 162 receives an operation to select a measurement ROI (stepS207: No), the determining function 162 is in a standby state.

Further, the statistic value calculating function 163 calculates anaverage of the shear velocity values in the measurement ROI (step S208)and causes the calculated average of the shear velocity values to bedisplayed (step S209). Because the processes at steps S208 and S209 arethe same as the processes at steps S107 and S108 in FIG. 6, explanationsthereof will be omitted.

The processing procedure in FIG. 9 explained above is merely an example.Possible embodiments are not limited to this example. For instance,although the example is explained above in which the statistic value(the average of the shear velocity values) is calculated only withrespect to the determined measurement ROI, possible embodiments are notlimited to this example. For instance, a statistic value may becalculated for each of all the measurement candidate ROIs. With thisarrangement, for example, the operator is able to select a measurementROI from among the measurement candidate ROIs, by referring to thestatistic value of each of the measurement candidate ROIs.

As explained above, in the ultrasound diagnosis apparatus 1 according tothe second embodiment, the analyzing function 121 is configured tocalculate the tissue characteristic parameter values by analyzing theresult of the scan performed on the patient P. After that, the indexvalue calculating function 161 is configured to calculate the indexvalue related to the variance among the tissue characteristic parametervalues. Subsequently, the determining function 162 is configured todetermining the measurement candidate regions on the basis of the indexvalues. After that, the display controlling function 164 is configuredto cause the measurement candidate regions to be displayed over theimage based on the tissue characteristic parameter values. With thesearrangements, the ultrasound diagnosis apparatus 1 presents theplurality of measurement candidate ROIs containing no noise to theoperator. The ultrasound diagnosis apparatus 1 is thus able to analyzethe tissue characteristic with an excellent level of precision, realizedwith a simple operation.

Other Embodiments

It is possible to carry out the present disclosure in other variousmodes besides the embodiments described above.

Changing the Size of the Segmented Regions

For example, in the embodiments described above, the example isexplained in which the segmented regions having the size set in advanceare used. However, possible embodiments are not limited to is example.For instance, the operator is able to arbitrarily change the size of thesegmented regions.

FIG. 10 is a flowchart illustrating a processing procedure performed bythe ultrasound diagnosis apparatus 1 according to another embodiment. Ofthe processing procedure illustrated in FIG. 10, because the processesat steps S301 through S307 are the same as the processes at steps S201through S207 in FIG. 9, explanations thereof will be omitted.

As illustrated in FIG. 10, unless the processing circuitry 160 receivesa selection of a measurement ROI (step S307: No), the processingcircuitry 160 is in a standby state. In that situation, when havingreceived, from the operator, an instruction indicating that the size ofthe segmented regions should be changed (step S308: Yes), the inputdevice 102 outputs the received instruction to the processing circuitry160. After that, when having received the instruction of the operatorfrom the input device 102, the processing circuitry 160 proceeds to theprocess at step S303. In other words, the index value calculatingfunction 161 changes the size of the segmented regions in response tothe instruction from the operator and further re-calculates a variancevalue among the shear velocity values by using the segmented regionshaving the post-change size.

FIG. 11 is a drawing for explaining a process performed by the indexvalue calculating function 161 according to said another embodiment. Asillustrated in FIG. 11, the index value calculating function 161 changesthe size of the segmented regions on three levels, in accordance with aninstruction from the operator. More specifically, the size of thesegmented regions is set in advance on one of the three levels, namelysegmented regions (small), segmented regions (medium), and segmentedregions (large). It is desirable to set the size of the segmentedregions so that the display ROI is divisible (so that there is noremainder). Further, the size of the segmented regions is kept inassociation with an operation performed on a dial switch. For example,by operating the dial switch, the operator is able to change the size ofthe segmented regions. Further, the index value calculating function 161sets the size to one selected from among the segmented regions (small),the segmented regions (medium), and the segmented regions (large) inresponse to the instruction from the operator. Subsequently, by usingthe segmented regions having the set size, the index value calculatingfunction 161 re-calculates a variance value among the shear velocityvalues (step S303). After that, the processes at step S304 andthereafter are sequentially performed.

On the contrary, when the size of the segmented regions is not to bechanged (step S308: No), the processing circuitry 160 proceeds to theprocess at step S307. In other words, unless the processing circuitry160 receives either a selection of a measurement ROI or a change made tothe size of the segmented regions, the processing circuitry 160 is in astandby state. Because the processes at steps S309 and S310 are the sameas the processes at steps S208 and S209 in FIG. 9, explanations thereofwill be omitted.

As a result, the ultrasound diagnosis apparatus 1 is able to set thesegmented regions having the size arbitrarily selected by the operator.Accordingly, the operator is able to change the size of the segmentedregions to an arbitrary size in accordance with a desired size of themeasurement ROI, for example, by setting the size of the segmentedregions to 10 mm (or a divisor of 10 mm) when the desired size of themeasurement ROI is 10 mm.

Changing the Threshold Value for the Variance

For example, in the embodiments described above, the example isexplained in which the threshold value set in advance is used. However,possible embodiments are not limited to this example. For instance, theoperator is able to arbitrarily change the threshold value.

FIG. 12 is a flowchart illustrating a processing procedure performed bythe ultrasound diagnosis apparatus 1 according to yet anotherembodiment. Of the processing procedure illustrated in FIG. 12, becausethe processes at steps S401 through S407 are the same as the processesat steps S201 through S207 in FIG. 9, explanations thereof will beomitted.

As illustrated in FIG. 12, unless the processing circuitry 160 receivesa selection of a measurement ROI (step S407: No), the processingcircuitry 160 is in a standby state. In that situation, when havingreceived an operation to change the threshold value from the operator(step S408: Yes), the input device 102 outputs the received instructionto the processing circuitry 160. After that, when having received theinstruction of the operator from the input device 102, the processingcircuitry 160 proceeds to the process at step S404. In other words, thedetermining function 162 changes the threshold value in response to theoperation from the operator and further compares the variance among theshear velocity values in each of the segmented regions, by using thepost-change threshold value (step S404).

FIG. 13 is a chart for explaining a process performed by the determiningfunction 162 according to said yet another embodiment. In the example inFIG. 13, a histogram is displayed for the purpose of receiving anoperation to change the threshold value from the operator. In thehistogram, the horizontal direction corresponds to a standard deviation(SD) (a variance value), whereas the vertical direction corresponds tofrequency (the number of segmented regions). In the histogram, thethreshold value is expressed as a line in the vertical direction. InFIG. 13, positioned on the right-hand side of the line is a regiondetermined to have large variance, whereas positioned on the left-handside of the line is a region determined to have small variance.

In this situation, by operating the input device 102 such as a mouse,the operator moves the position of the line indicating the thresholdvalue either to the left or to the right. When having received theoperation to change the position of the line, the determining function162 changes the threshold value to a value corresponding to the positionof the line designated by the received operation. After that, thedetermining function 162 compares the post-change threshold value withthe variance value among the shear velocity values of each of thesegmented regions (step S404). Subsequently, the processes at step S405and thereafter are sequentially performed.

On the contrary, when the threshold value is not to be changed (stepS408: No), the processing circuitry 160 proceeds to the process at stepS407. In other words, unless the processing circuitry 160 receiveseither a selection of a measurement ROI or a change made to thethreshold value, the processing circuitry 160 is in a standby state.Because the processes at steps S409 and S410 are the same as theprocesses at steps S208 and S209 in FIG. 9, explanations thereof will beomitted.

With these arrangements, the ultrasound diagnosis apparatus 1 is able tochange the threshold value to a value arbitrarily determined by theoperator. Consequently, for example, the operator is able to set anappropriate threshold value in accordance with the patient's sitesubject to the measuring process. The process of changing the thresholdvalue described above is merely an example. Possible embodiments are notlimited to this example. For instance, the determining function 162 maychange the threshold value in accordance with the patient's site subjectto the measuring process. For example, an arrangement is acceptable inwhich an appropriate threshold value is registered in advance for eachof various sites, so that the determining function 162 determines athreshold value by reading a threshold value corresponding to the sitedesignated by the operator.

An Application of a Machine Learning Scheme

In the embodiments described above, the example is explained in whichthe measurement ROI (or the measurement candidate ROI) is determined byusing the index values related to the variance among the tissuecharacteristic parameter values. However, possible embodiments are notlimited to this example. For instance, the ultrasound diagnosisapparatus 1 is also capable of determining the measurement ROI (or themeasurement candidate ROI) by applying a machine learning scheme toinformation about a distribution of the tissue characteristic parametervalues.

More specifically, the index value calculating function 161 obtainsdistribution information of the tissue characteristic parameter valueswith respect to each of the plurality of sub-regions included in theregion of interest. Further, the index value calculating function 161calculates an index value indicating a degree of stability of the tissuecharacteristic parameter values of each of the sub-regions, by using thedistribution information of the tissue characteristic parameter valuesas an input to a trained machine learning scheme. Further, by comparingthe index value of each of the plurality of sub-regions with a thresholdvalue, the determining function 162 determines a measurement region (ora measurement candidate region).

FIG. 14 is a drawing for explaining a process performed by theultrasound diagnosis apparatus 1 according to yet another embodiment.FIG. 14 illustrates details of the process performed by the ultrasounddiagnosis apparatus 1 according to said yet another embodimentsequentially in the order of steps S10 through S14.

As illustrated in FIG. 14, at step S10, the index value calculatingfunction 161 divides a firmness image corresponding to a display ROIinto a plurality of sub-regions (segmented regions). Because thisprocess performed by the index value calculating function 161 is thesame as the process performed by the index value calculating function161 explained with reference to FIG. 2, explanation thereof will beomitted.

At step S11, the index value calculating function 161 generates ahistogram for each of the segmented regions. For example, the indexvalue calculating function 161 generates the histogram by plottinglevels of firmness of the pixels included in each of the segmentedregions. More specifically, in the histograms, the vertical axiscorresponds to frequency (the number of pixels), whereas the horizontalaxis corresponds to the level of firmness (the shear velocity value).Although the histograms in the three patterns are illustrated in thepresent example, possible histograms are not limited to those in thisexample. Further, the histograms serve as an example of the distributioninformation of the tissue characteristic parameter values.

At step S12, the index value calculating function 161 uses thehistograms of the segmented regions as an input to the machine learningscheme. The machine learning scheme has learned in advance acorrespondence relationship between various shapes of histograms andstability scores (degrees of stability) corresponding to the histogramshapes. In this situation, each of the stability scores is an indexvalue indicating how stable the levels of firmness in the segmentedregion are (how constant the levels of firmness are).

For example, as illustrated in the chart in the bottom section of stepS11, an ideal segmented region containing no noise exhibits a histogramhaving a protruding shape, because the levels of firmness of the pixelsare close to a certain value (the variance is small). In that situation,a larger value is given as the stability score. In contrast, asillustrated in the charts in the middle and the top sections of stepS11, the larger the noise in the segmented region is, the flatter theshape of the histogram becomes, because the levels of firmness of thepixels in the segmented region do not exhibit a constant value (thevariance is large). Thus, the flatter the histogram is, the smallervalue is given as the stability score.

In other words, when the index value calculating function 161 inputs thehistograms of the segmented regions to the machine learning scheme, themachine learning scheme outputs stability scores corresponding to theshapes of the input histograms. In the present example, the machinelearning scheme is created by the operator (or a designer of theultrasound diagnosis apparatus 1) in advance.

At step S13, the index value calculating function 161 calculates astability score of each of the segmented regions. For example, the indexvalue calculating function 161 assigns the stability scores output bythe machine learning scheme to the segmented regions. In other words,the index value calculating function 161 assigns the stability score “5:Recommended” to any of the segmented regions exhibiting a histogramhaving a protruding shape. Further, the index value calculating function161 assigns the stability score “1: Not Recommended” to any of thesegmented regions exhibiting a histogram having a flat shape. Further,the index value calculating function 161 assigns the stability score “3:Passable” to any of the segmented regions exhibiting a histogram havinga shape somewhere in the middle of a protruding shape and a flat shape.In this manner, the index value calculating function 161 assigns astability score to each of the segmented regions included in the displayROI. The stability scores serve as an example of the degree ofstability.

At step S14, the determining function 162 generates a stability scoremap. For example, the determining function 162 generates the stabilityscore map by using the stability scores of the segmented regionscalculated by the index value calculating function 161. In the presentexample, the stability score map is information obtained by expressingeach of the stability scores of the segmented regions in a correspondingposition within the display ROI. In the example in FIG. 14, suchsegmented regions that each have the stability score “5: Recommended”are indicated with “black dots”, while such segmented regions that eachhave the stability score “3: Passable” are indicated with “white dots”,and such segmented regions that each have the stability score “1: NotRecommended” are indicated with “triangles”.

For example, the determining function 162 determines a measurement ROIon the basis of the stability score map. In one example, the determiningfunction 162 determines at least one measurement ROI having an arbitraryshape from within a region combining together a plurality of segmentedregions each having a stability score equal to or higher than athreshold value. In this situation, when the threshold value is “5”, thedetermining function 162 determines at least one measurement ROI havingan arbitrary shape, from within the region combining together theplurality of segmented regions indicated with the “black dots”. Becausethe processes performed after the measurement ROI is determined are thesame as those explained in the embodiments above, explanations thereofwill be omitted.

The configuration illustrated in FIG. 14 is merely an example. Possibleembodiments are not limited to the example illustrated in FIG. 14. Forinstance, although FIG. 14 illustrates the example in which themeasurement ROI is determined from within the region combining togetherthe segmented regions indicated with the “black dots”, possibleembodiments are not limited to this example. For instance, thedetermining function 162 may determine a measurement ROI from within aregion combining together the segmented regions indicated with the“black dots” and the “white dots”. In other words, the stability scoresto be used for determining the measurement ROI may arbitrarily be set bythe operator.

Further, although FIG. 14 illustrates the example in which the stabilityscores are evaluated in the three grades, possible embodiments are notlimited to this example. For instance, the index value calculatingfunction 161 may evaluate the stability scores in two grades or in fouror more grades. In other words, the operator may arbitrarily set thenumber of grades in which the stability scores are evaluated.

Further, although FIG. 14 illustrates the example in which themeasurement ROI is determined, possible embodiments are not limited tothis example. For instance, the determining function 162 may determine ameasurement candidate ROI instead of the measurement ROI, as explainedin the second embodiment.

An Evaluation Using Multiple Grades

Further, for instance, although in the embodiment above (FIG. 3), theexample is explained in which the magnitude of the variance is evaluatedin the two grades (whether the variance is large or small) while usingonly the one threshold value, possible embodiments are not limited tothis example. For instance, when making a judgment by using twothreshold values, the index value calculating function 161 may evaluatethe variance in three grades such as “Recommended”, “Passable”, and “NotRecommended”, as illustrated in FIG. 14. In that situation, ameasurement ROI may be determined by using the “Recommended” regions,similarly to the example in FIG. 14. Alternatively, a measurement ROImay be determined by using the “Recommended” and the “Passable” regions.In another example, the index value calculating function 161 may make anevaluation in four or more grades by using three or more thresholdvalues.

The Analyzing Apparatus

Further, for example, in the embodiments described above, the ultrasounddiagnosis apparatus is explained as an example of the analyzingapparatus. However, possible embodiments are not limited to thisexample. For instance, as the analyzing apparatus, other medical imagediagnosis apparatuses besides the ultrasound diagnosis apparatus 1 arealso applicable, such as X-ray diagnosis apparatuses, X-ray CTapparatuses, MRI apparatuses, SPECT apparatuses, PET apparatuses,SPECT-CT apparatuses in which a SPECT apparatus and an X-ray CTapparatus are integrated together, PET-CT apparatuses in which a PETapparatus and an X-ray CT apparatus are integrated together, or a groupmade up of any of these apparatuses. Further, as the analyzingapparatus, not only medical image diagnosis apparatuses, but alsoarbitrary information processing apparatuses (computers) capable ofprocessing medical information are applicable.

FIG. 15 is a block diagram illustrating an exemplary configuration of aninformation processing apparatus 200 according to yet anotherembodiment. The information processing apparatus 200 is, for example, anapparatus such as a personal computer or a workstation.

As illustrated in FIG. 15, the information processing apparatus 200includes an input device 201, a display 202, a storage circuitry 210,and a processing circuitry 220. The input device 201, the display 202,the storage circuitry 210, and the processing circuitry 220 areconnected to one another so as to be able to communicate with oneanother.

The input device 201 is an input device such as a mouse, a keyboard, atouch panel, and/or the like, configured to receive various types ofinstructions and setting requests from the operator. The display 202 isa display device configured to display medical images and a GUI used bythe operator to input the various types of setting requests through theinput device 201.

The storage circuitry 210 may be, for example, a NOT-AND (NAND) flashmemory or a Hard Disk Drive (HDD) and is configured to store thereinvarious types of programs used for displaying medical image data and theGUI as well as information used by the programs.

The processing circuitry 220 an electronic device (a processor)configured to control the entirety of processes performed by theinformation processing apparatus 200. The processing circuitry 220executes an analyzing function 221, an index value calculating function222, a determining function 223, a statistic value calculating function224, and a display controlling function 225. The processing functionsexecuted by the processing circuitry 220 are, for example, recorded inthe storage circuitry 210 in the form of computer-executable programs.By reading and executing the programs, the processing circuitry 220 isconfigured to realize the functions corresponding to the read programs.

For example, the analyzing function 221 is capable of performingbasically the same processes as those performed by the analyzingfunction 121 illustrated in FIG. 1. The index value calculating function222 is capable of performing basically the same processes as thoseperformed by the index value calculating function 161 illustrated inFIG. 1. The determining function 223 is capable of performing basicallythe same processes as those performed by the determining function 162illustrated in FIG. 1. The statistic value calculating function 224 iscapable of performing basically the same processes as those performed bythe statistic value calculating function 163 illustrated in FIG. 1. Thedisplay controlling function 225 is capable of performing basically thesame processes as those performed by the display controlling function164 illustrated in FIG. 1. With these arrangements, the informationprocessing apparatus 200 is able to analyze the tissue characteristicwith an excellent level of precision, similarly to the ultrasounddiagnosis apparatus 1 explained above.

Tissue Characteristic Parameters

Further, for instance, in the embodiments described above, the exampleis explained in which the shear velocity values of the shear wave areused as an example of the tissue characteristic parameter values.However, possible embodiments are not limited to this example. Forinstance, instead of the shear velocity values of the shear wave,arrival times of the shear wave explained above may be used.Alternatively, elasticity modulus values may be used.

Further, for example, the ultrasound diagnosis apparatus 1 is able touse any of the following as a tissue characteristic parameter:“velocity” of a blood flow based on a color Doppler method; a“displacement” of a tissue based on a Tissue Doppler Imaging (TDI)method; a “strain” of a tissue based on strain elastography to expressin an image a strain caused by small vibration to press and release apatient's tissue; an “attenuation” of an ultrasound wave propagatingthrough a patient's body expressed as an attenuation image; and a“brightness local variance value” indicating a degree of deviation froma Rayleigh distribution representing a distribution of signal amplitudesof a reception signal. The analyzing function 121 calculates, as thetissue characteristic parameter values, one selected from among shearvelocity values, arrival times, elasticity modulus values, velocityvalues, displacement values, strain values, attenuation values, andbrightness local variance values, with respect to the positions wherethe scan was performed. Further, besides the tissue characteristicparameter obtained by the ultrasound diagnosis apparatus 1, it ispossible to use, as a tissue characteristic parameter, a parameterindicating firmness levels based on elastography obtained by using anMRI apparatus or a parameter related to a substance identificationscheme realized by a dual energy CT analysis that utilizes differencesin an X-ray attenuation coefficient among various substances. In otherwords, it is possible to use any parameter as long as the parameter isnot a parameter used in a tomographic image of a tissue in a patient'sbody, but is a parameter expressing a characteristic of a tissue.

Further, the constituent elements of the apparatuses and the devicesillustrated in the drawings are based on functional concepts. Thus, itis not necessary to physically configure the constituent elements asindicated in the drawings. In other words, the specific modes ofdistribution and integration of the apparatuses and the devices are notlimited to those illustrated in the drawings. It is acceptable tofunctionally or physically distribute or integrate all or a part of theapparatuses and the devices in any arbitrary units, depending on variousloads and the status of use. Further, all or an arbitrary part of theprocessing functions performed by the apparatuses and the devices may berealized by a CPU and a computer program analyzed and executed by theCPU or may be realized as hardware using wired logic.

With regard to the processes explained in the above embodiments, it isacceptable to manually perform all or a part of the processes describedas being performed automatically. Conversely, by using a method that ispublicly known, it is also acceptable to automatically perform all or apart of the processes described as being performed manually. Further,unless noted otherwise, it is acceptable to arbitrarily modify any ofthe processing procedures, the controlling procedures, specific names,and various information including various types of data and parametersthat are presented in the above text and the drawings.

Further, it is possible to realize the analyzing method explained in theabove embodiments, by causing a computer such as a personal computer ora workstation to execute an analyzing computer program (hereinafter,“analyzing program”) prepared in advance. It is possible distribute theanalyzing program via a network such as the Internet. Further, theanalyzing program may be executed as being recorded on acomputer-readable recording medium such as a hard disk, a flexible disk(FD), a Compact Disk Read-Only Memory (CD-ROM), a Magneto-Optical (MO)disk, a Digital Versatile Disk (DVD), or the like and being read fromthe recording medium by a computer.

According to at least one aspect of the embodiments described above, itis possible to analyze the tissue characteristic with an excellent levelof precision.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An analyzing apparatus comprising processingcircuitry configured: to calculate, with respect to each of a pluralityof positions within a region of interest for a scan performed when ashear wave propagates through a body of a patient, at least one of shearvelocity values, arrival times, elasticity modulus values, velocityvalues, displacement values, and strain values, by analyzing a result ofthe scan performed on the patient; to determine a measurement region inthe region of interest based on the calculated values; and to calculatea statistic value of at least one of shear velocity values, arrivaltimes, elasticity modulus values, velocity values, displacement values,and strain values in the measurement region.
 2. The analyzing apparatusaccording to claim 1, wherein the processing circuitry calculates anindex value related to variance among the calculated values with respectto each of a plurality of sub-regions included in the region ofinterest, and the processing circuitry determines the measurement regionby comparing the index value of each of the plurality of sub-regionswith a threshold value.
 3. The analyzing apparatus according to claim 2,wherein the processing circuitry causes the sub-regions to be displayedinto an image based on the calculated values.
 4. The analyzing apparatusaccording to claim 2, wherein the processing circuitry receives an inputto change a size of the sub-regions.
 5. The analyzing apparatusaccording to claim 2, wherein the processing circuitry receives an inputto change the threshold value.
 6. The analyzing apparatus according toclaim 5, wherein the processing circuitry determines the measurementregion by comparing the index value of each of the plurality ofsub-regions with the changed threshold value.
 7. The analyzing apparatusaccording to claim 5, wherein the processing circuitry receives, as theinput, an instruction of an operator to change the threshold value. 8.The analyzing apparatus according to claim 5, wherein the processingcircuitry is further configured to re-determine a measurement region inthe region of interest based on comparison between the changed thresholdvalue and at least one of the calculated values.
 9. The analyzingapparatus according to claim 8, wherein the processing circuitry isfurther configured to cause the re-determined measurement region to bedisplayed into an image based on the calculated values.
 10. Theanalyzing apparatus according to claim 2, wherein the processingcircuitry changes the threshold value in accordance with a site of thepatient subject to a measuring process.
 11. The analyzing apparatusaccording to claim 2, wherein, as the index value, the processingcircuitry calculates one selected from among a variance value, astandard deviation, and a residual sum of squares, with respect to thecalculated values.
 12. The analyzing apparatus according to claim 2,wherein the processing circuitry determines the threshold value based ona site of the patient subject to the scan.
 13. The analyzing apparatusaccording to claim 12, wherein an arrangement is acceptable in which anappropriate threshold value is registered in advance for each of varioussites, and the processing circuitry determines the threshold value byreading a threshold value corresponding to the site designated by anoperator.
 14. The analyzing apparatus according to claim 1, wherein theprocessing circuitry obtains distribution information of the calculatedvalues with respect to each of a plurality of sub-regions included inthe region of interest, the processing circuitry calculates an indexvalue indicating a degree of stability of the calculated values for eachof the sub-regions, by using the distribution information as an input toa trained machine learning scheme, and the processing circuitrydetermines the measurement region by comparing the index value of eachof the plurality of sub-regions with a threshold value.
 15. Theanalyzing apparatus according to claim 1, wherein the processingcircuitry causes the measurement region to be displayed into an imagebased on the calculated values.
 16. The analyzing apparatus according toclaim 1, wherein, as the statistic value, the processing circuitrycalculates one selected from among an average value, a median, avariance value, a standard deviation, and a residual sum of squares,with respect to the calculated values.
 17. The analyzing apparatusaccording to claim 1, wherein the processing circuitry calculates, byanalyzing a result of a MRI scan performed on the patient, at least oneof the shear velocity values, the arrival times, the elasticity modulusvalues, the velocity values, the displacement values, and the strainvalues.
 18. The analyzing apparatus according to claim 1, wherein thescan is performed such that an ultrasound probe transmits anobservation-purpose pulse in order to observe the shear wave caused by apush pulse transmitted from the ultrasound probe.
 19. An analyzingapparatus comprising processing circuitry configured: to calculate, withrespect to each of a plurality of positions within a region of interestfor a scan performed when a shear wave propagates through a body of apatient, at least one of shear velocity values, arrival times,elasticity modulus values, velocity values, displacement values, andstrain values, by analyzing a result of the scan performed on thepatient; to determine a measurement candidate region in the region ofinterest based on the calculated values; and to cause the measurementcandidate region to be displayed into an image.
 20. The analyzingapparatus according to claim 19, wherein the processing circuitrycalculates an index value related to variance among the calculatedvalues, with respect to each of a plurality of sub-regions included inthe region of interest, and the processing circuitry determines themeasurement candidate region by comparing the index value of each of theplurality of sub-regions with a threshold value.
 21. The analyzingapparatus according to claim 20, wherein the processing circuitry causesthe sub-regions to be displayed into the image based on the calculatedvalues.
 22. The analyzing apparatus according to claim 20, wherein theprocessing circuitry determines one or more measurement candidateregions, from within a region combining together two or more of thesub-regions that are each determined, as a result of the comparison, tohave a small value as the variance.
 23. The analyzing apparatusaccording to claim 22, wherein the processing circuitry determines oneor more measurement candidate regions of which a quantity is determinedin advance.
 24. The analyzing apparatus according to claim 22, wherein,as the measurement candidate region, the processing circuitry determinesa circular region having a largest diameter.
 25. The analyzing apparatusaccording to claim 19, wherein the processing circuitry obtainsdistribution information of the calculated values with respect to eachof a plurality of sub-regions included in the region of interest, theprocessing circuitry calculates an index value indicating a degree ofstability of the calculated values for each of the sub-regions, by usingthe distribution information as an input to a trained machine learningscheme, and the processing circuitry determines the measurementcandidate region by comparing the index value of each of the pluralityof sub-regions with a threshold value.
 26. The analyzing apparatusaccording to claim 25, wherein the processing circuitry determines oneor more measurement candidate regions from within a region combiningtogether two or more of the sub-regions that are each determined, as aresult of the comparison, to exhibit a high level as the degree ofstability.
 27. The analyzing apparatus according to claim 19, whereinthe processing circuitry determines the measurement candidate regionhaving a shape and a size that are set in advance.
 28. The analyzingapparatus according to claim 19, wherein the image is at least one ofB-mode image, a firmness image, and a region image in which firmnesslevels of tissue in the body of the patient are displayed.
 29. Theanalyzing apparatus according to claim 19, wherein the scan is performedsuch that an ultrasound probe transmits an observation-purpose pulse inorder to observe the shear wave caused by a push pulse transmitted fromthe ultrasound probe.
 30. An analyzing method comprising: calculating,with respect to each of a plurality of positions within a region ofinterest for a scan performed when a shear wave propagates through abody of a patient, at least one of shear velocity values, arrival times,elasticity modulus values, velocity values, displacement values, andstrain values, by analyzing a result of the scan performed on thepatient; determining a measurement region in the region of interestbased on the calculated values; and calculating a statistic value of atleast one of shear velocity values, arrival times, elasticity modulusvalues, velocity values, displacement values, and strain values in themeasurement region.
 31. The analyzing method according to claim 30,wherein the scan is performed such that an ultrasound probe transmits anobservation-purpose pulse in order to observe the shear wave caused by apush pulse transmitted from the ultrasound probe.
 32. An analyzingmethod comprising: calculating, with respect to each of a plurality ofpositions within a region of interest for a scan performed when a shearwave propagates through a body of a patient, at least one of shearvelocity values, arrival times, elasticity modulus values, velocityvalues, displacement values, and strain values, by analyzing a result ofthe scan performed on the patient; determining a measurement candidateregion in the region of interest based on the calculated values: andcausing the measurement candidate region to be displayed into an image.33. The analyzing method according to claim 32, wherein the scan isperformed such that an ultrasound probe transmits an observation-purposepulse in order to observe the shear wave caused by a push pulsetransmitted from the ultrasound probe.
 34. An analyzing apparatuscomprising processing circuitry configured: to calculate, with respectto each of a plurality of positions within a region of interest for ascan performed when a shear wave propagates through a body of a patient,parameter values based on a shear wave, by analyzing a result of thescan performed on the patient; to determine a measurement region in theregion of interest based on the calculated values; and to calculate astatistic value of the parameter values in the measurement region. 35.The analyzing apparatus according to claim 34, wherein the scan isperformed such that an ultrasound probe transmits an observation-purposepulse in order to observe the shear wave caused by a push pulsetransmitted from the ultrasound probe.
 36. An analyzing apparatuscomprising processing circuitry configured: to calculate, with respectto each of a plurality of positions within a region of interest for ascan performed when a shear wave propagates through a body of a patient,parameter values based on a shear wave, by analyzing a result of thescan performed on the patient; to determine a plurality of measurementcandidate regions in the region of interest based on the calculatedvalues; and to select a measurement region from the plurality ofmeasurement candidate regions.
 37. The analyzing apparatus according toclaim 36, wherein the scan is performed such that an ultrasound probetransmits an observation-purpose pulse in order to observe the shearwave caused by a push pulse transmitted from the ultrasound probe.